Download Advances in Intelligent Data Analysis XI: 11th International by Gavin C. Cawley (auth.), Jaakko Hollmén, Frank Klawonn, PDF

By Gavin C. Cawley (auth.), Jaakko Hollmén, Frank Klawonn, Allan Tucker (eds.)

This booklet constitutes the refereed lawsuits of the eleventh overseas convention on clever information research, IDA 2012, held in Helsinki, Finland, in October 2012. The 32 revised complete papers provided including three invited papers have been rigorously reviewed and chosen from 88 submissions. All present elements of clever information research are addressed, together with clever help for modeling and reading facts from advanced, dynamical platforms. The papers concentrate on novel functions of IDA suggestions to, e.g., networked electronic details structures; novel modes of knowledge acquisition and the linked concerns; robustness and scalability problems with clever information research ideas; and visualization and dissemination results.

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Advances in Intelligent Data Analysis XI: 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings

This booklet constitutes the refereed court cases of the eleventh overseas convention on clever facts research, IDA 2012, held in Helsinki, Finland, in October 2012. The 32 revised complete papers awarded including three invited papers have been rigorously reviewed and chosen from 88 submissions. All present facets of clever facts research are addressed, together with clever aid for modeling and interpreting facts from advanced, dynamical structures.

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Additional resources for Advances in Intelligent Data Analysis XI: 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings

Sample text

The focus of our experiments lies on how the quality of the solutions changes, as we vary the number of used parallel resources. To evaluate the average performance of the algorithms with respect to the parallelization parameter, for each data set each algorithm was run randomized 50 times and the mean and the standard deviation of the size of the cover was reported. If our initial hypothesis is correct, the average performance should go up (here: the size of the cover should go down) when more parallel resources are used.

Our tables show that increasing the number of noise features has resulted in slightly better results for a few datasets. As the increase appeared in only some experiments, this may not have statistical significance. We intend to address this in future research. The processing time presented in all tables relates to a single run. The nondeterministic algorithms tended to have lower processing times; however, these were run 50 times. Table 1. Experiments with the iris and hepatitis datasets. The results are shown per row for the original iris dataset, with +2 and +4 noise features, and the original hepatitis dataset, with +10 and + 20 noise features.

We only consider very specific programs. For, we rely on cover functions and expression evaluation. For this reason, our approach is actually close to the Minimum Description Length principle [4]. MDL is a principle to select a model from a given set of models. This is very much what we do. 1. There is actually more to comprehensibility than our note there. Clearly, by allowing any program, we can discover very rich structure. However, rich structure is not necessarily easy to grasp. What if computing the answer to a query takes a very long time?

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